Slide 71

Module 12 - Analyze Data
Slide 72
9-step Evaluation Process
Step 6: Analyze Data
Slide 73
What is Data Analysis?

Data collected during program evaluation are compiled and analyzed (counting;
number crunching)

Inferences are drawn as to why some results occurred and others did not

Can be very complex depending on your evaluation questions

We will focus on simple things that can be done without expert consultants
Slide 74
Types of Data Analysis
Simple Frequency Counts
Area: 9th grade career plans completed
Frequency: 29
Area: Employers that rate student math skills as good to excellent
Frequency: 23
Area: Number of students exploring non traditional occupations
Frequency: 6
Area: Number of 10th grade teachers that incorporate career information into the curriculum
Frequency: 8
Area: Number of truancy reports second semester, 2006
Frequency: 125
Slide 75
Types of Data Analysis
Sort by Relevant Categories
Area: 9th grade career plans completed
Frequency: Males - 15 Females - 14
Area: Employers that rate student math skills as
Frequency: Poor to Average - 17 Good to Excellent - 23
Area: Number of students exploring non traditional occupations
Frequency: Males - 1 Females - 2
Area: Number of truancy reports second semester, 2007
Frequency: Blacks - 12 Whites - 14 Hispanics - 14 Other - 4
Slide 76
Types of Data Analysis
Calculate Percentages - Exercise 9 (p.13 of Workbook)
Area: Percent of male and female 9th grade students with completed career plans
Frequency: Males - 73% Females - 67%
Area: Employers that rate student math skills as
Frequency: Poor to Average - 42% Good to Excellent - 58%
Area: Percent of students exploring non traditional occupations
Frequency: 21%
Area: Percent of truancy reports second semester, 2007
Frequency: Blacks - 23% Whites - 41% Hispanics - 24% Other - 12%
Slide 77
Types of Data Analysis
Showing Change or Differences
Percent of Employers Who Rate Student Math Skills as Good to Excellent
2005 35%
2007 58 %
Time on the "Choosing Careers" Computerized System by 10th Grade Students
Before Career Class (Minutes) 29
After Career Class (Minutes) 77
Percent change in bullying incidents as compared to school year 2001-2002
School Year 2002-2003 0%
School Year 2003-2004 -15%
School Year 2004-2005 -18%
Slide 78
Types of Data Analysis Reaching an Objective or Goal
Percent of Students Identifying Their Interests by May 2005
Goal 100
Actual 64
Percent of Teachers Incorporating Information About Occupations into Their Curriculum
Goal 90
Actual 78
Slide 79
Types of Data Analysis Observing Trends
Percent of American HS Graduates in Full Employment
2 years after graduation 15%
4 years after graduation 25%
6 years after graduation 75%
Percent of American HS Graduates in Full Employment
Students with Mentoring Program 2 years after graduation 10%
Students with No Mentoring Program 2 years after graduation 25%
Students with Mentoring Program 4 years after graduation 25%
Students with No Mentoring Program 4 years after graduation 30%
Students with Mentoring Program 6 years after graduation 65%
Students with No Mentoring Program 6 years after graduation 45%
Slide 80
Types of Data Analysis Graph Results
EMPLOYMENT RATES OF STUDENTS AFTER GRADUATION
Students with Mentoring Program 2 years after graduation 10%
Students with No Mentoring Program 2 years after graduation 25%
Students with Mentoring Program 4 years after graduation 25%
Students with No Mentoring Program 4 years after graduation 30%
Students with Mentoring Program 6 years after graduation 65%
Students with No Mentoring Program 6 years after graduation 45%
Slide 81
Types of Data Analysis Calculate Averages - Exercise 10 (p. 14 of Workbook)
Average SAT Score
Students in Experimental Program 1250 Students in Regular Program 1170
Average Standard Score on the ASVAB
Students in Experimental Program 52 Students in Regular Program 51
Average Grade Point Average
Students in Experimental Program 3.5 Students in Regular Program 3.1
Average Mentor Rating
Students in Experimental Program 3.1 Students in Regular Program 2.5
Slide 82
Types of Data Analysis Calculate Weighted Averages
Survey of Career Goals of Students in American High School
1. As a result of my career class, I am better prepared to make a career decision.
1 Strongly Disagree. 2 Disagree. 3 Neither Agree or Disagree. 4 Agree. 5 Strongly Agree.
2. As a result of my career class, I know what I want to do after high school.
1 Strongly Disagree. 2 Disagree. 3 Neither Agree or Disagree. 4 Agree. 5 Strongly Agree.
3. I know how good health habits impact my career development.
1 Strongly Disagree. 2 Disagree. 3 Neither Agree or Disagree. 4 Agree. 5 Strongly Agree.
4. My school's career day helped me think about occupations I never considered before.
1 Strongly Disagree. 2 Disagree. 3 Neither Agree or Disagree. 4 Agree. 5 Strongly Agree.
5. I know that it doesn't matter if I am male or female when it comes to selecting an occupation.
1 Strongly Disagree. 2 Disagree. 3 Neither Agree or Disagree. 4 Agree. 5 Strongly Agree.
Slide 83
Types of Data Analysis Calculate Weighted Averages
Item#1. Number of Students Answering
1: 3 2: 4 3: 6 4: 3 5: 9 TOTAL: 25
Number of Students x the Response Value
1: 3 2: 4 3: 18 4: 12 5: 45 TOTAL: 86
Total Points Divided by Number of Students
TOTAL: 3.44
Item #2. Number of Students Answering
1: 9 2: 7 3: 3 4: 4 5: 2 TOTAL: 25
Number of Students x the Response Value
1: 9 2: 14 3: 9 4: 16 5: 10 TOTAL: 58
Total Points Divided by Number of Students
TOTAL: 2.32
Item #3. Number of Students Answering
1: 1 2: 2 3: 5 4: 3 5: 14 TOTAL: 25
Number of Students x the Response Value
1: 1 2: 4 3: 15 4: 12 5: 70 TOTAL: 102
Total Points Divided by Number of Students
TOTAL: 4.08
Item #4. Number of Students Answering
1: 0 2: 0 3: 0 4: 12 5: 13 TOTAL: 25
Number of Students x the Response Value
1: 0 2: 0 3: 0 4: 48 5: 65 TOTAL: 113
Total Points Divided by Number of Students
TOTAL: 4.52
Item #5. Number of Students Answering
1: 10 2: 7 3: 3 4: 5 5: 0 TOTAL: 25
Number of Students x the Response Value
1: 10 2: 14 3: 9 4: 20 5: 0 TOTAL: 53
Total Points Divided by Number of Students
TOTAL: 2.12
End of Chart
Types of Data Analysis: Calculate Weighted Averages
To calculate the weighted average, you tally the number of students that strongly disagree (value
1), disagree (value 2), neither agree or disagree (value 3), agree (value 4) or strongly agree
(value 5).
25 students answered this 5-item survey.
Simply multiply the response value times the number of students answering each option. For the
first item, 3 students strongly disagreed so multiplying 3 times 1 is 3. The number of students
answering disagree was 4 times the value of that response is 8, 6 students answered neither
agree nor disagree with a value of 3 giving us 18 points, 3 students answered agree with a value
of 4 giving the total points of 12, and 9 students answered strongly agree with a value of 5, giving
45 total points.
The next step is to add the total number of points. In this example, adding 3, 8, 18, 12, and 45
gives us 86 points. Divide that by the total number of students, giving you a weighted average of
3.44. A weighted average then takes into account not only the number of students involved, but
also the strength of their belief or opinion.
Slide 84
Types of Data Analysis
Rank Order Weighted Averages
My school's career day helped me think about occupations I had never considered before.
Weighted Score: 4.52
Rank: 1
I know how good health habits impact on career development.
Weighted Score: 4.08
Rank: 2
As a result of my career class, I am better prepared to make a career decision.
Weighted Score: 3.44
Rank: 3
As a result of my career class, I know what I want to do after high school.
Weighted Score: 2.32
Rank: 4
I know that it doesn't matter if I am male or female when it comes to selecting an occupation.
Weighted Score: 2.12
Rank: 5
Slide 85
Types of Data Analysis Graph Weighted Averages
DEGREE OF ACCOMPLISHMENT OF CAREER OBJECTIVES - AMERICAN HIGH SCHOOL
Weighted Score: 4.52
Rank: 1
Weighted Score: 4.08
Rank: 2
Weighted Score: 3.44
Rank: 3
Weighted Score: 2.32
Rank: 4
Weighted Score: 2.12
Rank: 5
Slide 86
Using Focus Group/Interview Information
Qualitative findings from focus groups, extended response items, etc., should be
analyzed in a different way
Code words/frequency
Identify themes
Pull quotes
Summarize and draw conclusions